On quality of different annotation sources for gene expression analysis

Francesca Mulas, Tomaz Curk, Riccardo Bellazzi, Blaz Zupan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Mining of biomedical data increasingly relies on utility of knowledge repositories. In gene expression analysis, these are often used for gene labeling with an assumption that similarly annotated genes have similar expression profiles. In the paper we use this assumption to craft a method with which we scored six different annotation sources (e.g., Gene Ontology, PubMed, and MeSH annotations) for their utility in gene expression data analysis. Experiments show that the sources that include manual curation perform well and, for instance, score better than automatic annotation from gene-related PubMed abstracts. We also show that there is no clear winner, pointing at the need for methods that could successfully integrate annotations from different sources.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages421-425
Number of pages5
Volume5651 LNAI
DOIs
Publication statusPublished - 2009
Event12th Conference on Artificial Intelligence in Medicine, AIME 2009 - Verona, Italy
Duration: Jul 18 2009Jul 22 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5651 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other12th Conference on Artificial Intelligence in Medicine, AIME 2009
CountryItaly
CityVerona
Period7/18/097/22/09

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Mulas, F., Curk, T., Bellazzi, R., & Zupan, B. (2009). On quality of different annotation sources for gene expression analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5651 LNAI, pp. 421-425). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5651 LNAI). https://doi.org/10.1007/978-3-642-02976-9_60